Upload app.py

app.py

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+import streamlit as st
+import pandas as pd
+from src.data_preparation import load_data
+from src.model import initialize_openai, fine_tune_model, extract_keywords, rate_skills, compare_candidates
+
+st.title("MLE Trial Task")
+
+# Upload OpenAI API Key
+api_key = st.text_input("Enter your OpenAI API Key", type="password")
+initialize_openai(api_key)
+
+# Load training data
+data = load_data("data/train_dataset.csv")
+
+# Fine-tune the model
+fine_tuned_model = "gpt-4o-2024-08-06"
+
+# Streamlit app sections
+st.sidebar.title("Navigation")
+page = st.sidebar.radio("Go to", ["Evaluate Test Data", "Test with Random Data"])
+
+if page == "Evaluate Test Data":
+    st.title("Evaluate Test Data")
+    
+    uploaded_test_file = st.file_uploader("Upload Test Data CSV", type="csv")
+    
+    if uploaded_test_file:
+        test_data = pd.read_csv(uploaded_test_file)
+        
+        correct_predictions = 0
+        total_predictions = len(test_data)
+        
+        for index, row in test_data.iterrows():
+            job_description = row['role']
+            candidateA_resume = row['candidateAResume']
+            candidateB_resume = row['candidateBResume']
+            candidateA_transcript = row['candidateATranscript']
+            candidateB_transcript = row['candidateBTranscript']
+            winner_id = row['winnerId']
+            
+            candidateA_details = {
+                "keywords": extract_keywords(candidateA_resume, job_description, fine_tuned_model),
+                "skills": rate_skills(candidateA_transcript, job_description, fine_tuned_model),
+                "candidate_id": row['candidateAId']
+            }
+            candidateB_details = {
+                "keywords": extract_keywords(candidateB_resume, job_description, fine_tuned_model),
+                "skills": rate_skills(candidateB_transcript, job_description, fine_tuned_model),
+                "candidate_id": row['candidateBId']
+            }
+            
+            preferred_candidate = compare_candidates(candidateA_details, candidateB_details, job_description, fine_tuned_model)
+            print(f"Preferred Candidate: {preferred_candidate}", f"Winner ID: {winner_id}")
+            if preferred_candidate == winner_id:
+                correct_predictions += 1
+        
+        accuracy = correct_predictions / total_predictions
+        st.write(f"Accuracy: {accuracy}")
+        st.write(f"Preferred Candidates: {correct_predictions} out of {total_predictions}")
+
+elif page == "Test with Random Data":
+    st.title("Test with Random Data")
+    
+    job_description = st.text_area("Job Description")
+    candidateA_resume = st.text_area("Candidate A Resume")
+    candidateB_resume = st.text_area("Candidate B Resume")
+    candidateA_transcript = st.text_area("Candidate A Transcript")
+    candidateB_transcript = st.text_area("Candidate B Transcript")
+    
+    if st.button("Compare Candidates"):
+        candidateA_details = {
+            "keywords": extract_keywords(candidateA_resume, job_description, fine_tuned_model),
+            "skills": rate_skills(candidateA_transcript, job_description, fine_tuned_model)
+        }
+        candidateB_details = {
+            "keywords": extract_keywords(candidateB_resume, job_description, fine_tuned_model),
+            "skills": rate_skills(candidateB_transcript, job_description, fine_tuned_model)
+        }
+        
+        preferred_candidate = compare_candidates(candidateA_details, candidateB_details, job_description, fine_tuned_model)
+        st.write(f"Preferred Candidate: {preferred_candidate}")